To address the problem of privacy disclosure during data mining, a new privacy-preserving
decision tree classification construction model based on a differential privacy-protection …

In the problem of privacy-preserving collaborative data publishing, a central data publisher
is responsible for aggregating sensitive data from multiple parties and then anonymizing it …

Machine learning (ML) is increasingly being deployed in critical systems. The data
dependence of ML makes securing data used to train and test ML-enabled systems of …

Private record linkage protocols allow multiple parties to exchange matching records, which
refer to the same entities or have similar values, while keeping the non-matching ones …

Many models have been proposed to preserve data privacy for different data publishing
scenarios. Among these models,∊-differential privacy is receiving increasing attention …

Privacy-preserving distributed data fusion is a pretreatment process in data mining involving
security models. In this paper, we present a method of implementing multiparty data fusion …

With the rapid growth of the health data scale, the limited storage and computation
resources of wireless body area sensor networks (WBANs) is becoming a barrier to their …

How to achieve differential privacy in the distributed setting, where the dataset is distributed
among the istrustful parties, is an important problem. We consider in what condition can a …

Data synthesis is a promising solution to share data for various downstream analytic tasks
without exposing raw data. However, without a theoretical privacy guarantee, a synthetic …

E-health data release, which answers the statistical queries of the Electronic Health Records
(EHRs), has been widely adopted in modern health care services. However, since the EHRs …